• 1. About this Book
  • 2. 10 Reasons why Ecommerce Businesses Need to Have Google Analytics Set Up Correctly
  • 3. Quick Overview of Your Google Analytics Admin - Before You Set Up Your Ecommerce
  • 4. Setting Up Your “All Web Site Data” View in Google Analytics
  • 5. What the Heck are Parameters... And What do I do with the "Exclude Query Parameters" Field in Google Analytics?
  • 6. Adding Filtered Google Analytics Views Gives You Access to Better Marketing Data
  • 7. Setting up an “Include My Domain” Filter in Google Analytics
  • 8. Using Google Analytics Filters to Rid Yourself of Rage-Inducing Referral Spam
  • 9. Formulating Your IP Address Filter in Google Analytics
  • 10. Ensuring that Your Google Analytics Data is Accurate by Applying Lowercase Filters
  • 11. How to Remove Slashes From The End of your URLs in Google Analytics
  • 12. Fixing the Problem of Parameters in your Ecommerce URLS in Google Analytics
  • 13. Acquiring Your Ecommerce Store’s Unique Parameter List for Google Analytics
  • 14. How to Turn your Ecommerce Parameters into Custom Dimensions in Google Analytics
  • 15. Using your Parameter Custom Dimensions to Discover Ecommerce Opportunities
  • 16. Key Google Analytics Settings You Might Have Overlooked for your Ecommerce Configuration
  • 17. What are the Google Analytics Ecommerce Settings For and How are They Set Up?
  • 18. How to Turn on Ecommerce Tracking in Google Analytics
  • 19. Google Analytics Enhanced Ecommerce on popular Ecommerce Platforms
  • 20. Manually Adding Google Analytics Standard Ecommerce Transaction Tracking Code
  • 21. Manually Adding Google Analytics Enhanced Ecommerce Transaction Tracking Code
  • 22. Implementing Enhanced Ecommerce Features to Collect Game Changing Data For Your Ecommerce Store
  • 23. How Do You Use the Ecommerce Reports Built into Google Analytics?
  • 24. What is the Google Analytics Ecommerce Overview Report and What Should You Use It For?
  • 25. What is the Shopping Behavior Report and What Should You Use It For?
  • 26. The Importance of the Checkout Behavior Report in Google Analytics
  • 27. What is the Product Performance Report Used for in Google Analytics?
  • 28. This post has been deleted
  • 29. How can you see Individual Ecommerce Transactions in Google Analytics?
  • 30. What is the Time to Purchase Report in Google Analytics Used For?
  • 31. Deep-dive your Product Sales with the Google Analytics Product List Report
  • 32. Setting Ecommerce Goals in Google Analytics and Why This is So Important
  • 33. Adding Your Ecommerce Goals to Google Analytics
  • 34. Using Google Analytics Goals to Boost Your Ecommerce Conversion Rate
  • 35. Using the Model Comparison Tool in Google Analytics
  • 36. Segmenting Users - A Powerful Tool for Providing Data Insights
  • 37. Building Segments Using the Shopping Behavior Report
  • 38. How to Use the Segment Builder in Google Analytics
  • 39. How to Build Specific Criteria using Google Analytics' Segment Builder
  • 40. Google Analytics Segment Examples to Enhance Your Ecommerce Sales
  • 41. How to use Segmentation Analysis to Identify Opportunities and Increase Conversion
  • 42. Making the Most of the Demographics of Users When Looking at Ecommerce Data
  • 43. Google Analytics Segmentation Example - Transacted vs Did Not Transact
  • 44. Taking your Segmentation Analysis Further
  • 45. Bonus: Six Reasons Why Your Ecommerce Store Needs Google Tag Manager
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    Google Analytics Model Comparison

    Using the Model Comparison Tool in Google Analytics

    The Model Comparison tool is an aspect of Google Analytics that helps you to attribute completed transactions to specific marketing campaigns. This is really useful, because you need to know whether any specific marketing campaign was good Return on Investment or poor Return on Investment (ROI).

    Essentially you can find out which campaign led to a sale using some heuristics built into the Google Analytics Model Comparison tool, and see whether changing the model changes the results.

    Some examples of attribution models include:

    • Last Click – i.e. the last click to the website was the source/channel attributed to the transaction
    • First Click – i.e. the first click to the website was the source/channel attributed to the transaction
    • Linear – i.e. all the clicks to the website were attributed to the transaction
    • Time Decay – all the clicks to the website were attributed to the transaction, but the more recent ones got a higher weighting

    It’s important to note that by default Google Analytics uses the Last Interaction Model, which has some issues. Last Interaction means that the sale is attributed to whichever marketing source the visitor used in the session in which they made the purchase. However, if the visitor came to your website via various different marketing methods this may not be an accurate representation of how they found your site.

    For example, if a visitor found your store via Organic Search and then went away and thought about it, then came back a week later via an Adwords Ad, which marketing source brought in the lead? Google Analytics by default would say the Adwords ad did it, but you might want to give attribution to the Organic Search as well.

    Another issue is that if your visitor took too long to fill in their credit card details, or to set up a post-purchase payment processing system such as Afterpay, the session can time out during the purchase. With Last Click attribution this purchase is attributed to Direct.

    In my opinion, the most accurate attribution model is the Linear Attribution. Linear averages out the attribution score for each of the marketing sources leading up to the sale. You can see an example of this below:

    So you can see in the example above that there were 258 Paid Search conversions that came directly when looking through the Last Interaction Model but if you apply a Linear Attribution Model it reweights that to 301.92. Similarly, Display had 1 conversion for the Last Interaction Model and that is reweighted to 5.08 in the Linear Model.

    This lets you calculate a more accurate cost per acquisition. In this example, the cost per acquisition if we used Last Interaction Model was $9.61 for Paid Search. Whereas the cost per acquisition if we use the Linear model ends up being $8.22.

    Similarly for Display, the cost per acquisition was revised from $1,461.61 to $287.79 when applying a Linear Model. This shows how you can easily find out how much money you’re spending on different ads. But it’s important to note that if you don’t have a lot of transactions it is best to optimise for an earlier stage in the Shopping Funnel such as Add to Cart. You can do that by selecting a different Goal from the pull-down list.

    Typically I find that people first find out about a product from a PPC campaign, but after looking at it and thinking about it, end up completing the transaction via Organic Search or Direct. Alternatively the visitor may come back via a vanity ad by searching for your business name.

    The Model Comparison tool can provide you with some very useful insight into how well your advertising campaigns are working, and what sort of return you’re getting from them. Use it to identify if any marketing campaigns have been overlooked due to bringing visitors in earlier in the buying cycle.

    Want to get 8-10X ROAS from Google Ads?